ArticleNAR genomics and bioinformatics2024
SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
- A Landscape Analysis of Human SUMOylation.Molecular & cellular proteomics : MCP · 2026Article
- Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction.Scientific reports · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.bioRxiv : the preprint server for biology · 2025Article
- PCBert-Kla: an efficient prediction method for lysine lactylation sites based on ProtBert and fusion of physicochemical features.Briefings in bioinformatics · 2025Article
- Proteins need extra attention: improving the predictive power of protein language models on mutational datasets with hint tokens.NAR genomics and bioinformatics · 2025Article
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites.BioData mining · 2025Article
- Multimodal deep learning for predicting protein ubiquitination sites.Bioinformatics advances · 2025Article
- TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.Briefings in bioinformatics · 2024Article
- Prediction of human O-linked glycosylation sites using stacked generalization and embeddings from pre-trained protein language model.Bioinformatics (Oxford, England) · 2024Article
- DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2 and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64%, 73.36%, 0.48% and 74.00%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.